Training of ANFIS with simulated annealing algorithm on flexural buckling load prediction of aluminium alloy columns

In this study, we propose a simulated annealing algorithm (SA) to train an adaptive neurofuzzy inference system (ANFIS). We performed different types of optimization algorithms such as genetic algorithm (GA), SA and artificial bee colony algorithm on two different problem types. Then, we measured the performance of these algorithms. First, we applied optimization algorithms on eight numerical benchmark functions which are sphere, axis parallel hyper-ellipsoid, Rosenbrock, Rastrigin, Schwefel, Griewank, sum of different powers and Ackley functions. After that, the training of ANFIS is carried out by mentioned optimization algorithms to predict the strength of heat-treated fine-drawn aluminium composite columns defeated by flexural bending. In summary, the accuracy of the proposed soft computing model was compared with the accuracy of the results of existing methods in the literature. It is seen that the training of ANFIS with the SA has more accuracy. Keywords: Soft computing, ANFIS, simulated annealing, flexural buckling, aluminium alloy columns.

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A Hybrid Artificial Bee Colony Algorithm Combined with Simulated Annealing Algorithm for Traveling Salesman Problem

2013 International Conference on Information Science and Cloud Computing Companion ◽

10.1109/iscc-c.2013.13 ◽

2013 ◽

Cited By ~ 5

Author(s):

Pei Shi ◽

Suling Jia

Keyword(s):

Simulated Annealing ◽

Traveling Salesman Problem ◽

Artificial Bee Colony Algorithm ◽

Artificial Bee Colony ◽

Simulated Annealing Algorithm ◽

Traveling Salesman ◽

Bee Colony ◽

Annealing Algorithm

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The Application and Comparison of Two Different Optimization Algorithms in the Optimization Design of Hydrofoil

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.178-181.2871 ◽

2012 ◽

Vol 178-181 ◽

pp. 2871-2876

Author(s):

Chao Wang ◽

Feng Feng ◽

Xin Chang ◽

Chun Yu Guo ◽

Yang Hao Liu

Keyword(s):

Genetic Algorithm ◽

Simulated Annealing ◽

Optimization Design ◽

Simulated Annealing Algorithm ◽

Optimization Algorithms ◽

Optimization Method ◽

Flow Solver ◽

Propeller Design ◽

Section Design ◽

Annealing Algorithm

Hydrofoil is the important part of ship design and diverse motion equipment. The optimization design of hydrofoil section on lift-to-drag radio with genetic algorithm (GA) and simulated annealing algorithm are demonstrated, and the method on the hydrofoil section design of the propeller design will be done. Objective function and fitness of every individual are provided by flow solver of panel method. The optimization method on design of hydrofoil section on lift-to-drag is successfully used. The optimization results show the combination of optimization algorithm is feasible at the optimal design of hydrofoil sections. What’s more, a comparison between two different optimization algorithms is made, a conclusion that the simulated annealing algorithm is better then the genetic algorithm is obtained.

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Optimization Techniques for the Economic Dispatch Problem in Various Generation Plant

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.768.323 ◽

2013 ◽

Vol 768 ◽

pp. 323-328

Author(s):

K. Thenmalar ◽

A. Allirani

Keyword(s):

Simulated Annealing ◽

Firefly Algorithm ◽

Artificial Bee Colony ◽

Simulated Annealing Algorithm ◽

Economic Dispatch ◽

Bee Colony ◽

Adaptive Simulated Annealing ◽

Annealing Algorithm ◽

Adaptive Simulated Annealing Algorithm ◽

Dynamic Economic Dispatch

The dynamic economic dispatch (DED) occupies important place in a power systems operation and control. It aims to determine the optimal power outputs of on-line generating units in order to meet the load demand and reducing the fuel cost. The nonlinear and non convex characteristics are more common in the DED problem. Therefore, obtaining a optimal solution presents a challenge. In the proposed system, firefly algorithm, Adaptive simulated annealing algorithm, artificial bee colony (ABC) algorithm a recently introduced population-based technique is utilized to solve the DED problem. A general formulation of this algorithm is presented together with an analytical mathematical modeling to solve this problem by a single equivalent objective function. The results are compared with those obtained by alternative techniques proposed by the literature in order to show that it is capable of yielding good optimal solutions with proper selection of control parameters. Keywords: ABC-Artificial Bee Colony Algorithm, DED-Dynamic Economic Dispatch, FA-firefly algorithm, ASA-Adaptive Simulated annealing algorithm

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Selection of Bending Parameters for Minimal Spring-Back Using an ANFIS Model and Simulated Annealing Algorithm

Journal of Manufacturing Science and Engineering ◽

10.1115/1.4004139 ◽

2011 ◽

Vol 133 (3) ◽

Cited By ~ 9

Author(s):

H. Baseri ◽

B. Rahmani ◽

M. Bakhshi-Jooybari

Keyword(s):

Simulated Annealing ◽

Simulated Annealing Algorithm ◽

Fuzzy Inference ◽

Sheet Thickness ◽

Spring Back ◽

Anfis Model ◽

Inference System ◽

Bending Process ◽

Annealing Algorithm ◽

Tip Radius

In this research, a simulated annealing algorithm was used to minimize the spring-back in V-die bending process. First, an adaptive neuro-fuzzy inference system (ANFIS) model was developed using the data generated based on experimental observations. The output parameter of the ANFIS model is spring-back and the input parameters are sheet thickness, sheet orientation, and punch tip radius. The performance of the ANFIS model in training and testing sets is compared with those observations. The results indicated that the ANFIS model can be applied successfully for prediction of spring-back. Then, the ANFIS model was used as a function in simulated annealing algorithm to minimize the spring-back. The results showed that the proposed model has an acceptable performance to optimize the bending process.

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